# 07_item-clustering.R
#
# content: (1) Read data
#           (1.1) Read log event data
#           (1.2) Read infos for PM for infos
#           (1.3) Extract additional infos for clustering
#          (2) Clustering
#          (3) Visualization with pictures
#
# input:  results/haum/eventlogs_pre-corona_cleaned.RData
#         results/haum/pn_infos_items.csv
# output: results/haum/eventlogs_pre-corona_item-clusters.csv
#
# last mod: 2024-03-06

# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")

library(bupaverse)
library(factoextra)

#--------------- (1) Read data ---------------

#--------------- (1.1) Read log event data ---------------

load("results/haum/eventlogs_pre-corona_cleaned.RData")

#--------------- (1.2) Read infos for PM for items ---------------

datitem <- read.table("results/haum/pn_infos_items.csv", header = TRUE,
                      sep = ";", row.names = 1)

#--------------- (1.3) Extract additional infos for clustering ---------------

time_minmax_ms <- function(subdata) {
  subdata$min_time <- min(subdata$timeMs.start)
    if (all(is.na(subdata$timeMs.stop))) {
      subdata$max_time <- NA
    } else {
       subdata$max_time <- max(subdata$timeMs.stop, na.rm = TRUE)
    }
  subdata
}
# TODO: Move to helper file

# Get average duration per path
dat_split <- split(dat, ~ path)
dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
dat_minmax <- dplyr::bind_rows(dat_list)

datpath <- aggregate(duration ~ item + path, dat, mean, na.action = NULL)
datpath$min_time <- aggregate(min_time ~ path, dat_minmax, unique, na.action = NULL)$min_time
datpath$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
datpath$duration <- datpath$max_time - datpath$min_time

datitem$duration <- aggregate(duration ~ item, datpath, mean)$duration
datitem$distance <- aggregate(distance ~ item, dat, mean)$distance
datitem$scaleSize <- aggregate(scaleSize ~ item, dat, mean)$scaleSize
datitem$rotationDegree <- aggregate(rotationDegree ~ item, dat, mean)$rotationDegree
datitem$npaths <- aggregate(path ~ item, dat, function(x) length(unique(x)))$path
datitem$ncases <- aggregate(case ~ item, dat, function(x) length(unique(x)))$case
datitem$nmoves <- aggregate(event ~ item, dat, table)$event[,"move"]
datitem$nflipCard <- aggregate(event ~ item, dat, table)$event[,"flipCard"]
datitem$nopenTopic <- aggregate(event ~ item, dat, table)$event[,"openTopic"]
datitem$nopenPopup <- aggregate(event ~ item, dat, table)$event[,"openPopup"]

#--------------- (2) Clustering ---------------

df <- datitem[, c("precision", "generalizability", "nvariants", "duration",
                  "distance", "scaleSize", "rotationDegree", "npaths",
                  "ncases", "nmoves", "nopenTopic", "nopenPopup")] |>
      scale()

dist_mat <- dist(df)

heatmap(as.matrix(dist_mat))

# Choosing best linkage method
method <- c(average = "average", single = "single", complete = "complete",
            ward = "ward")

hcs <- lapply(method, function(x) cluster::agnes(dist_mat, method = x))
acs <- sapply(hcs, function(x) x$ac)

# Dendograms
par(mfrow=c(4,2))
for (hc in hcs) plot(hc,  main = "")

hc <- hcs$ward

k <- 4    # number of clusters

mycols <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")

grp <- cutree(hc, k = k)
datitem$grp <- grp

fviz_dend(hc, k = k,
          cex = 0.5,
          k_colors = mycols,
          #type = "phylogenic",
          rect = TRUE
)

p <- fviz_cluster(list(data = df, cluster = grp),
                  palette = mycols,
                  ellipse.type = "convex",
                  repel = TRUE,
                  show.clust.cent = FALSE, ggtheme = theme_bw())
p

aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
                ncases, nmoves, nflipCard, nopenTopic, nopenPopup) ~ grp,
          datitem, mean)


aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
                ncases, nmoves, nflipCard, nopenTopic, nopenPopup) ~ grp,
          datitem, max)


# Something like a scree plot (??)
plot(rev(hc$height), type = "b", pch = 16, cex = .5)

datitem$item <- sprintf("%03d",
                        as.numeric(gsub("item_([0-9]{3})", "\\1", row.names(datitem))))

res <- merge(dat, datitem[, c("item", "grp")], by = "item", all.x = TRUE)
res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]

# Look at clusters
par(mfrow = c(2,2))
vioplot::vioplot(duration ~ grp, res)
vioplot::vioplot(distance ~ grp, res)
vioplot::vioplot(scaleSize ~ grp, res)
vioplot::vioplot(rotationDegree ~ grp, res)

write.table(res,
            file = "results/haum/eventlogs_pre-corona_item-clusters.csv",
            sep = ";",
            quote = FALSE,
            row.names = FALSE)

# DFGs for clusters
res$start <- res$date.start
res$complete <- res$date.stop

for (cluster in sort(unique(res$grp))) {

  alog <- activitylog(res[res$grp == cluster, ],
                      case_id     = "path",
                      activity_id = "event",
                      resource_id = "item",
                      timestamps  = c("start", "complete"))
  
  dfg <- process_map(alog,
                     type_nodes = frequency("relative", color_scale = "Greys"),
                     sec_nodes  = frequency("absolute"),
                     type_edges = frequency("relative", color_edges = "#FF6900"),
                     sec_edges  = frequency("absolute"),
                     rankdir    = "LR",
                     render     = FALSE)
  export_map(dfg,
             file_name = paste0("results/processmaps/dfg_cluster", cluster, "_R.pdf"),
             file_type = "pdf",
             title     = paste("DFG Cluster", cluster))
}

#--------------- (3) Visualization with pictures ---------------

library(png)
library(jpeg)
library(grid)

pdf("results/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10)
#png("results/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300)

par(mai = c(.4,.4,.1,.1), mgp = c(2.4, 1, 0))

plot(y ~ x, p$data, type = "n", ylim = c(-3.5, 2.8), xlim = c(-5, 10),
     xlab = "", ylab = "")

for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {

  if (item == "125") {

    pic <- readJPEG(paste0("../data/haum/ContentEyevisit/eyevisit_cards_light/",
                          item, "/", item, ".jpg"))
  } else {
    pic <- readPNG(paste0("../data/haum/ContentEyevisit/eyevisit_cards_light/",
                          item, "/", item, ".png"))
  }

  img <- as.raster(pic[,,1:3])

  x <- p$data$x[sprintf("%03d", as.numeric(rownames(p$data))) == item]
  y <- p$data$y[sprintf("%03d", as.numeric(rownames(p$data))) == item]

  points(x, y,
         col = mycols[p$data$cluster[sprintf("%03d", as.numeric(rownames(p$data))) == item]],
         cex = 9,
         pch = 15)

  rasterImage(img,
              xleft = x - .45,
              xright = x + .45,
              ybottom = y - .2,
              ytop = y + .2)

}
legend("topright", paste("Cluster", 1:k), col = mycols, pch = 15, bty = "n")

dev.off()